Projected Real‐World Effectiveness of Using Aggressive Low‐Density Lipoprotein Cholesterol Targets Among Elderly Statin Users Following Acute Coronary Syndromes in Canada
Bibliographic record
Abstract
Background The extent to which outcome benefits may be achieved through the implementation of aggressive low‐density lipoprotein (LDL) cholesterol targets in real world settings remains unknown, especially among elderly statin users following acute coronary syndromes. Methods and Results A population‐based cohort study consisting of 19 544 post‐acute coronary syndrome statin‐users aged ≥66 years between January 1, 2017 and March 31, 2014 was used to project the number of adverse outcome events (acute myocardial infarction or death from any cause) that could be prevented if all post‐acute coronary syndrome elderly statin users were treated to 1 of 2 LDL cholesterol target levels (≤50 and ≤70 mg/dL). The number of preventable adverse outcomes was estimated by using model‐based expected event probabilities as derived from Cox Proportional hazards models. In total, 61.6% and 25.5% of the elderly patients met LDL cholesterol targets of ≤70 and ≤50 mg/dL, respectively, based on current management. No more than 2.3 adverse events per 1000 elderly statin users (95% confidence interval: −0.7 to 5.4, P =0.62) could be prevented over 8.1 years if all patients were to be treated from current LDL cholesterol levels to either of the 2 LDL cholesterol targets of 70 or 50 mg/dL. Conclusions The number of acute myocardial infarctions or death that could be prevented through the implementation of LDL cholesterol targets with statins is negligible among an elderly post‐acute coronary syndrome population. Such findings may have implications for the applicability of newer agents, such as proprotein convertase subtilisin/kexin type‐9‐ inhibitors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".